Dynamic Clustering of Maps in Autonomous Agents

The problem of organizing and exploiting spatial knowledge for navigation is an important issue in the field of autonomous mobile systems. In particular, partitioning the environment map into connected clusters allows for significant topological features to be captured and enables decomposition of path-planning tasks through a divide-and-conquer policy. Clustering by discovery is a procedure for identifying clusters in a map being learned by exploration as the agent moves within the environment, and yields a valid clustering of the available knowledge at each exploration step. In this work, we define a fitness measure for clustering and propose two incremental heuristic algorithms to maximize it. Both algorithms determine clusters dynamically according to a set of topological and metric criteria. The first one is aimed at locally minimizing a measure of "scattering" of the entities belonging to clusters, and partially rearranges the existing clusters at each exploration step. The second estimates the positions and dimensions of clusters according to a global map of density. The two algorithms are compared in terms of optimality, efficiency, robustness, and stability.